CN112834456A - Near-infrared online quality detection method for white paeony roots - Google Patents
Near-infrared online quality detection method for white paeony roots Download PDFInfo
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- G—PHYSICS
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Abstract
The invention discloses a near-infrared online quality detection method for white paeony root, which comprises the following steps: (1) sample preparation: taking radix paeoniae alba decoction piece samples of different producing areas and different batches; (2) collecting near infrared spectrum data: simultaneously collecting and recording a near-infrared spectrogram of a white paeony root sample and a near-infrared spectrogram of the white paeony root after powdering; (3) preprocessing the spectral data: respectively adopting an original spectrum, a first-order derivation, a second-order derivation, a multivariate scattering correction, vector normalization or convolution smoothing filtering to preprocess the near infrared spectrum data of the white paeony root sample before and after powdering; (4) and (3) performing model screening by adopting a Convolutional Neural Network (CNN) or a Partial Least Squares Regression (PLSR) method, and establishing a white paeony root quantitative correction model. The method is quick and simple to operate, and the established model is accurate and reliable and can be used for quantitative analysis of the paeoniflorin and moisture content in the white paeony root decoction pieces.
Description
Technical Field
The invention belongs to the technical field of medicinal material detection; in particular to a Partial Least Squares Regression (PLSR) -based near-infrared quality detection method for white paeony roots.
Background
Radix Paeoniae alba is Paeonia lactiflora pall of RanunculaceaePaeonia lactifloraThe dry root of pall has the efficacies of nourishing blood, regulating menstruation, astringing yin, arresting sweating, softening liver, relieving pain and calming liver yang, and is clinically used for treating blood deficiency, chlorosis, irregular menstruation, spontaneous perspiration, night sweat, hypochondriac pain, abdominal pain, limb contracture pain, headache, dizziness and the like. The chemical components of the white paeony root mainly comprise monoterpene, triterpenes, volatile oil and flavonoids, wherein the monoterpene and glycoside components thereof are found at most and comprise more than 20 of paeoniflorin, hydroxyl paeoniflorin, albiflorin, benzoylpaeoniflorin and the like, and the monoterpene and glycoside components are also the most main physiological active substances in the white paeony root. Modern pharmacological research shows that the pharmacological actions of the white paeony root mainly comprise anti-inflammation, liver protection, pain relief, blood nourishing and the like.
The quality of the radix paeoniae alba decoction pieces is controlled and evaluated by adopting high performance liquid chromatography and paeoniflorin as an index component in 'Chinese pharmacopoeia' of 2020 edition, and the water content is measured by adopting a drying method. The method has high accuracy, but the requirements of rapid control and high-flux detection of the decoction piece process in large-scale production of enterprises are difficult to meet in practical application. In recent years, Near-infrared spectroscopy (NIR) is widely used in the analysis industry of traditional Chinese medicines, and plays an important role in controlling the quality of genuine medicinal materials. The near infrared spectrum method can also be used for measuring the content of the traditional Chinese medicinal materials by combining a stoichiometric method and a near infrared spectrum method. NIR can provide frequency doubling and frequency combining absorption peak information of hydrogen-containing groups (OH, NH and CH) in compound molecules, and a chemometrics method can establish a correction model for sample information of near infrared spectrum and information measured by a standard method, so that rapid identification and content measurement of traditional Chinese medicines are realized. The Fourier transform near infrared (FT-NIR) analysis technology has the advantages of high analysis speed, high analysis efficiency, no need of special pretreatment of samples, high throughput and the like, so the method meets the requirements of on-line and quick detection of process control in production and becomes a beneficial supplement in the quality control aspect of the traditional Chinese medicine decoction pieces except for a pharmacopoeia method.
100 batches of white peony root decoction pieces in main production areas in China are taken as research objects, near infrared spectroscopy (NIR) spectra are associated with the content values of paeoniflorin and water through a chemometric method, a quantitative correction model is established, rapid quantitative analysis can be carried out on the white peony root decoction pieces, and the white peony root decoction pieces are powerfully served for process control and quality evaluation of decoction piece production.
Disclosure of Invention
In order to solve the technical problems, the invention provides a Partial Least Squares Regression (PLSR) -based near-infrared quality detection method for white paeony roots. The method is rapid and simple to operate, and the established model is accurate and reliable, and can be used for quantitative analysis of paeoniflorin and moisture content in the radix paeoniae alba decoction pieces.
In order to achieve the above purpose, the technical scheme provided by the invention is as follows:
a near-infrared online quality detection method for white paeony roots is characterized by comprising the following steps:
(1) sample preparation: taking radix paeoniae alba decoction piece samples of different producing areas and different batches;
(2) collecting near infrared spectrum data: simultaneously collecting and recording a near-infrared spectrogram of a white paeony root sample and a near-infrared spectrogram of the white paeony root after powdering;
(3) preprocessing the spectral data: respectively adopting an original spectrum, a first-order derivation, a second-order derivation, a multivariate scattering correction, vector normalization or convolution smoothing filtering to preprocess the near infrared spectrum data of the white paeony root sample before and after powdering;
(4) and (3) performing model screening by adopting a Convolutional Neural Network (CNN) or a Partial Least Squares Regression (PLSR) method, and establishing a white paeony root quantitative correction model.
Preferably, when the white peony root quantitative correction model is established by adopting the convolutional neural network in the step (4), the convolutional neural network model comprises a one-dimensional convolutional pooling layer, a two-dimensional convolutional pooling layer and a full-connection layer; the one-dimensional convolution pooling layer converts the input one-dimensional vector into a two-dimensional matrix; the one-dimensional convolution pooling layer comprises one-dimensional convolution operation, activation operation and pooling operation, wherein the number of convolution kernels of the one-dimensional convolution operation is 32, the size of the convolution kernels is 10 x1, and the step size of the convolution is 6. And the activation operation is completed by a modified linear Unit (ReLU) so that the neurons in the neural network have sparse activation; pooling operation using an average pooling model, taking 10 × 1 pooling windows each time, with step size set to 2; then, inputting the two-dimensional matrix obtained by the one-dimensional convolution pooling layer into the two-dimensional convolution pooling layer, and converting the two-dimensional matrix into a plurality of two-dimensional matrices through two-dimensional convolution operation, activation operation and two-dimensional pooling operation; the number of convolution kernels in the two-dimensional convolution operation is 64, and the size of the convolution kernels is 10 x 32; inputting a two-dimensional matrix obtained by a two-dimensional convolution pooling layer into a full-connection layer, and outputting a one-dimensional high-order vector;
and a learning rate attenuation mechanism is adopted, the initial value of the learning rate is set to be 0.03, the attenuation index is 1/e, the learning rate is gradually attenuated along with time in the network training process for dynamic adjustment, and the initial value of the weight of each layer follows zero-mean Gaussian distribution with the standard deviation of 0.1.
Preferably, the above-mentioned method for online quality detection of white peony root by near infrared is characterized in that the method for collecting the near infrared spectrum in step (2) is as follows: taking 10 g of white paeony root sample powder, filling and flattening the powder in a quartz sample tube, or directly selecting a relatively flat white paeony root decoction piece sample to ensure that the white paeony root sample is fully contacted with a near-infrared diffuse reflection optical fiber probe; the test environment temperature is 25 ℃, the relative humidity is 45-60%, the background built in the instrument is taken as the reference, the background is deducted, and the collection is carried outThe method is diffuse reflection by integrating sphere, and the wave number range is 12000-4000 cm-1Resolution of 8 cm-1The number of scanning times is 64, each sample is scanned for 2 times, and the average spectrum is taken as the near infrared spectrum of the sample.
Preferably, in the above method for detecting the quality of white peony root on line, the method for preprocessing the near infrared spectrum data in step (3) is a convolution smoothing filter method.
Preferably, in the above-mentioned near-infrared online quality detection method for white peony root, in step (4), a white peony root quantitative correction model is established by using a partial least squares regression method (PLSR).
Preferably, in the above near-infrared online quality detection method for white peony root, the optimal parameters for establishing a white peony root quantitative correction model by using a partial least squares regression method (PLSR) are as follows:
| component to be tested | Pretreatment method | Wave band/cm-1 | Number of major factors |
| Moisture content | Convolution smoothing filtering method | 5631.99~5057.22 | 7 |
| Paeoniflorin | Convolution smoothing filtering method | 11993.05~3996.40 | 7 |
Compared with the prior art, the invention has the technical advantages that:
1. compared with the traditional experience identification and index component content measurement, the method can be used for rapidly and nondestructively judging the quality of the white paeony root in real time.
2. The invention provides a new quality detection method for the quality control of the white paeony root, provides a new scientific basis for the quality supervision of the white paeony root in the market, and has wide application prospect.
3. The invention adopts Fourier transform Near Infrared (NIR) analysis technology to acquire a near infrared spectrogram of white paeony root decoction pieces, the sample pretreatment method comprises powdering and direct measurement, a chemometrics method is used for pretreating an original spectrum, the number of main factors is optimized, an optimal waveband is selected, and a NIR quantitative analysis model is established by a Partial Least Squares Regression (PLSR) method. Verification results show that the method provided by the invention is quick and simple to operate, the established model is accurate and reliable, and the method can be used for quantitative analysis of paeoniflorin and moisture content in the radix paeoniae alba decoction pieces.
Drawings
FIG. 1: collecting a near-infrared spectrogram after the radix paeoniae alba decoction pieces are pulverized;
FIG. 2: directly measuring the collected near infrared spectrogram of the radix paeoniae alba decoction pieces;
FIG. 3 typical chromatograms (1. paeoniflorin) are tested for the white peony sample (A) and the control (B).
Detailed Description
The present invention will be described below with reference to specific examples to make the technical aspects of the present invention easier to understand and grasp, but the present invention is not limited thereto. The experimental methods described in the following examples are all conventional methods unless otherwise specified; the reagents and materials are commercially available, unless otherwise specified.
Example 1
1. Experimental Material
1.1 Experimental drugs
The south Beijing sea-sourced traditional Chinese medicine decoction piece limited company provides 100 batches of radix paeoniae alba decoction piece samples of all main production areas in China, and specific information is shown in table 1.
TABLE 1100 sources of white peony root decoction pieces
| Producing area | Anhui Mazhou | Shanghai Shangqiu | Around the mouth of Henan | Shandong lotus leaf |
| Number of | 43 | 27 | 3 | 27 |
1.2 laboratory instruments and reagents
Bruker-sensor 37 fourier transform mid-infrared spectrometer (Bruker, germany) including OPUS5.0 software, Pbs detector; waters e2695 high performance liquid chromatograph (Waters corporation, USA) Waters2998 ultraviolet detector; one in ten thousand balance BSA2245-CW (Beijing Saedodus scientific instruments, Inc.); a one-hundred-thousandth balance model AG-285 (METTLER TOLEDO, Switzerland); KY-500E ultrasonic cleaner (Kunshan ultrasonic Instrument Co., Ltd.); HH-6 digital display constant temperature water bath (Changzhou national electric appliance Co., Ltd.); Milli-Q ultra pure water instruments (Millipore, USA); GeneSpeed X1 microcentrifuge (International trade for genetic Biotechnology, Shanghai, Inc.).
Paeoniflorin control (batch: X12A8C33672, purity > 98%) was purchased from Shanghai-derived leaf Biotech Ltd. Phosphoric acid was chromatographically pure (Shanghai Aladdin science and technology Co., Ltd.), acetonitrile was chromatographically pure (TEDIA Co., USA), and anhydrous ethanol was analytically pure (Susheng chemical Co., Ltd., Wuxi city).
2. Experimental methods and results
2.1 acquisition of the near Infrared Spectrum
Grinding 100 batches of white paeony root decoction piece samples, sieving by a No. 5 sieve, and simultaneously collecting and recording near infrared spectrograms of the samples after grinding and direct measurement. The specific operation is as follows: taking about 10 g of sample powder into a quartz sample tube, filling and flattening; or directly selecting a relatively flat decoction piece sample to make the sample fully contact with the near-infrared diffuse reflection optical fiber probe. The test environment temperature is 25 ℃, and the relative humidity is 45-60%. And taking the background in the instrument as a reference, and subtracting the background. The collection mode is diffuse reflection of an integrating sphere, and the wave number range is 12000-4000 cm-1Resolution of 8 cm-1The number of scanning times is 64, each sample is scanned for 2 times, and the average spectrum is taken as the near infrared spectrum of the sample. The near infrared spectra of the pulverized sample and the direct measurement are shown in fig. 1 and fig. 2, respectively.
2.2 content determination of paeoniflorin
2.2.1 preparation of test solutions
Taking about 0.1 g of white paeony root powder, precisely weighing, placing in a 50 mL measuring flask, adding 35 mL of diluted ethanol, carrying out ultrasonic treatment (power 240W and frequency 45 kHz) for 30 minutes, cooling, adding diluted ethanol to the scale, shaking up, filtering, taking a subsequent filtrate, centrifuging at 13000 r/min for 5 minutes, filtering a supernatant through a 0.22 mu m microporous membrane, and taking a subsequent filtrate to obtain the white paeony root powder. Each sample batch was prepared in parallel in 2 portions.
2.2.2 preparation of control solutions
Accurately weighing appropriate amount of penoniflorin reference substance in volumetric flask, adding methanol to constant volume to scale, shaking to obtain penoniflorin reference substance solution (54.18 μ g/mL), and storing at 4 deg.C for use.
2.2.3 chromatographic conditions
The mobile phase is A-acetonitrile: b-0.1% phosphoric acid in water (14: 86); a chromatographic column: c18Reversed phase HPLC column (Waters Xbridge, 4.6X 250 mm, 5 μm); flow rate: 1 mL/min; detection wavelength: 230 nm; the amount of the sample was 10. mu.L. A typical chromatogram for the test is shown in FIG. 3.
2.3 moisture determination
The moisture content of 100 samples was measured by the second drying method according to the moisture determination method (general rule 0832) in the fourth part of the Chinese pharmacopoeia of 2020.
3. Establishment of near infrared spectrum quantitative model
The near infrared spectrum quantitative model is designed by adopting Python programming language, the integrated development environment is Pycharm Community, and the operating system is Windows 7.
3.1 selection of correction models
A Convolutional Neural Network (CNN) and a Partial Least Squares Regression (PLSR) method are selected for model screening.
In both methods, 80% of samples are used as a training set and 20% of samples are used as a testing set in a modeling experiment. The CNN iteration was 2000 rounds and the data was selected over the full band of the near infrared spectrum. The PLSR data is also selected over the full band of the near infrared spectrum. Three indexes of Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are selected to evaluate the prediction capability of the model. The closer the index value is to 0, the more accurate the prediction result is suggested. The calculation results are shown in Table 2. To eliminate the effect of the randomness of the PLSR method, the values in Table 2 are the average of the 50 training-prediction results.
From the results, it is understood that the PLSR method is superior to the CNN method in predicting the water content and the paeoniflorin content. When the influence of the sample pretreatment process on the results is further compared, the predicted results of the pulverized decoction pieces are superior to those of the direct determination. Presumably due to better uniformity of texture of the sample after powdering. Because the deep learning method CNN does not show the superior performance in the fields of image processing and the like, the PLSR method is selected, and the near-infrared spectrogram acquired after the drinking tablet is pulverized is further processed to establish a quantitative correction model.
TABLE 2 comparison of evaluation indexes of CNN and PLSR methods
3.2 preprocessing of spectral data
Before a quantitative correction model is established, an original spectrum needs to be preprocessed, and the purpose is to reduce the influence of a plurality of factors such as high-frequency noise, scattered light, stray light, a sample state, instrument response and the like in the measurement process and improve the prediction accuracy of the model. The spectrum pretreatment method adopted by the invention comprises the following steps: the method comprises the steps of raw Spectrum (Spectrum), First derivative (1 stD), Second derivative (2 stD), Multivariate Scattering Correction (MSC), vector normalization (SNV) and convolution smoothing filter (S-G), and finally screening an optimal Spectrum preprocessing method by taking prediction mean square error (RMSEP) as an index. Namely: the smaller the RMSEP value is, the better the prompt prediction effect is, and the more reasonable the pretreatment method is. In order to ensure the fairness and reliability of results, the PLSR process of different pretreatment methods is repeated 50 times, the average value of 50 experimental results is taken (table 3), and the model prediction effect obtained by performing spectrum pretreatment on the pulverized water and content models by adopting convolution smoothing filtering (S-G) is found to be the best.
TABLE 3 influence of different spectrogram pretreatment methods on the model
| Spectrum preprocessing method | Moisture content | Paeoniflorin |
| Spectrum | 0.002548 | 0.004215 |
| 1stD | 0.005179 | 0.004831 |
| 2stD | 0.006161 | 0.005257 |
| MSC | 0.005651 | 0.006471 |
| SNV | 0.002703 | 0.004386 |
| S-G | 0.002422 | 0.004212 |
| MSC+SNV | 0.002686 | 0.004368 |
| S-G +MSC | 0.006514 | 0.009464 |
| S-G +SNV | 0.002771 | 0.004927 |
3.3 selection of spectral bands
By selecting appropriate spectral band, light can be reducedRedundant information in the spectrum improves the prediction accuracy of the model. The invention is provided withRThe Root Mean Square Error (RMSEP) and the corrected mean square error (RMSEC) are taken as the basis, and the optimal moisture waveband is 5631.99-5057.22 cm-1The best band of paeoniflorin is 11993.05-3996.40 cm-1. When the optimal wave band is selected, the modeling process of all the wave bands is repeated for 50 times to ensure the fairness and the accuracy of the result.
3.4 selection of the number of major factors
When modeling is performed by the PLSR method, different numbers of main factors have a large influence on the model prediction result. If the number of main factors is too high, an "overfitting" phenomenon occurs, but if the number of main factors is too small, the spectral information used is too small. Therefore, the invention hasRThe influence of the main factor number on the model is examined by taking the value and the RMSEP value as indexes, and the value and the RMSEP value are selectedRThe number of main factors with values closest to 1 and the lowest RMSEP value. As a result, the water content and the paeoniflorin content were both 7 in terms of the number of main factors. When the number of the main factors is selected, the modeling process of all the wave bands is repeated for 50 times to ensure the fairness and the accuracy of the result.
3.5 optimization results of the correction model
The parameters and evaluation indexes of the respective calibration models are shown in table 4.
TABLE 4 correction model parameters and evaluation indexes
| Component to be tested | Pretreatment method | Wave band/cm-1 | Number of major factors | R | RMSEP | RMSEC |
| Moisture content | S-G | 5631.99~5057.22 | 7 | 0.951 | 0.00234 | 0.00200 |
| Paeoniflorin | S-G | 11993.05~3996.40 | 7 | 0.802 | 0.00227 | 0.00335 |
3.6 validation of the model
Four additional random samples were also selected for external validation, each without taking part in the modeling. The samples were input into a quantitative model to obtain the predicted values, and the predictive power of the model was investigated by the relative deviation of the predicted values from the true values obtained by conventional methods (table 5). As can be seen from Table 5, the relative deviations between the predicted values and the true values of the models for verifying the water content and the paeoniflorin content of the randomly selected samples are less than 5%, which indicates that the prediction results are accurate and the models are successfully established.
Table 5 validation set sample prediction results
The above detailed description is specific to one possible embodiment of the present invention, and the embodiment is not intended to limit the scope of the present invention, and all equivalent implementations or modifications without departing from the scope of the present invention should be included in the technical scope of the present invention.
Claims (6)
1. A near-infrared online quality detection method for white paeony roots is characterized by comprising the following steps:
(1) sample preparation: taking radix paeoniae alba decoction piece samples of different producing areas and different batches;
(2) collecting near infrared spectrum data: simultaneously collecting and recording a near-infrared spectrogram of a white paeony root sample and a near-infrared spectrogram of the white paeony root after powdering;
(3) preprocessing the spectral data: respectively adopting an original spectrum, a first-order derivation, a second-order derivation, a multivariate scattering correction, vector normalization or convolution smoothing filtering to preprocess the near infrared spectrum data of the white paeony root sample before and after powdering;
(4) and (3) performing model screening by adopting a Convolutional Neural Network (CNN) or a Partial Least Squares Regression (PLSR) method, and establishing a white paeony root quantitative correction model.
2. The near-infrared online quality detection method for radix paeoniae alba according to claim 1, wherein in the step (4), when the convolutional neural network is adopted to establish the radix paeoniae alba quantitative correction model, the convolutional neural network model comprises a one-dimensional convolutional pooling layer, a two-dimensional convolutional pooling layer and a full-connection layer; the one-dimensional convolution pooling layer converts the input one-dimensional vector into a two-dimensional matrix; the one-dimensional convolution pooling layer comprises one-dimensional convolution operation, activation operation and pooling operation, wherein the number of convolution kernels of the one-dimensional convolution operation is 32, the size of the convolution kernels is 10 x1, the step size of the convolution is 6,
and the activation operation is completed by a modified linear Unit (ReLU) so that the neurons in the neural network have sparse activation; pooling operation using an average pooling model, taking 10 × 1 pooling windows each time, with step size set to 2; then, inputting the two-dimensional matrix obtained by the one-dimensional convolution pooling layer into the two-dimensional convolution pooling layer, and converting the two-dimensional matrix into a plurality of two-dimensional matrices through two-dimensional convolution operation, activation operation and two-dimensional pooling operation; the number of convolution kernels in the two-dimensional convolution operation is 64, and the size of the convolution kernels is 10 x 32; inputting a two-dimensional matrix obtained by a two-dimensional convolution pooling layer into a full-connection layer, and outputting a one-dimensional high-order vector;
and a learning rate attenuation mechanism is adopted, the initial value of the learning rate is set to be 0.03, the attenuation index is 1/e, the learning rate is gradually attenuated along with time in the network training process for dynamic adjustment, and the initial value of the weight of each layer follows zero-mean Gaussian distribution with the standard deviation of 0.1.
3. The method for the online near-infrared quality detection of the white paeony root according to claim 1, wherein the method for collecting the near-infrared spectrum in the step (2) comprises the following steps: taking 10 g of white paeony root sample powder, filling and flattening the powder in a quartz sample tube, or directly selecting a relatively flat white paeony root decoction piece sample to ensure that the white paeony root sample is fully contacted with a near-infrared diffuse reflection optical fiber probe; the test environment temperature is 25 ℃, the relative humidity is 45-60%, the background is deducted by taking the built-in background of the instrument as a reference, the collection mode is the diffuse reflection of an integrating sphere, and the wave number range is 12000-4000 cm-1Resolution of 8 cm-1The number of scanning times is 64, each sample is scanned for 2 times, and the average spectrum is taken as the near infrared spectrum of the sample.
4. The method for detecting the near-infrared online quality of the white paeony root according to claim 1, wherein the method for preprocessing the near-infrared spectrum data in the step (3) is a convolution smoothing filter method.
5. The near-infrared online quality detection method for radix paeoniae alba as claimed in claim 1, wherein in the step (4), a quantitative correction model for radix paeoniae alba is established by using a partial least squares regression method (PLSR).
6. The near-infrared online quality detection method for white peony root according to claim 5, characterized in that the optimal parameters for establishing the white peony root quantitative correction model by Partial Least Squares Regression (PLSR) are as follows:
。
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